# Getting started with socialSim

## Overview

The socialSim R package provides tools to simulate and analyse datasets of social interactions between individuals using hierarchical Bayesian models implemented in Stan.

This vignette demonstrates a typical workflow using three main functions:

1. `simulate_data()` – generate datasets of social interactions
2. `run_model()` – fit a Stan model to the simulated datasets
3. `summarise_results()` – evaluate bias and dispersion of estimated parameters

## 1. Simulating data

    sim <- simulate_data(
      ind = 200,
      partners = 4,
      repeats = 1,
      iterations = 5,
      B_0 = 1,
      psi = 0.3,
      Valpha = 0.2,
      Vepsilon = 0.1
    )

This creates a list of datasets representing repeated social interactions.
You can control study design components, variance components and correlations between direct and indirect effect.

## 2. Fitting a model

To analyse the data, fit one of the included Stan models:

    res <- run_model(sim, model = "Trait.stan", iter = 2000, cores = 4)

Importantly, you will need rstan installed for this step.

## 3. Summarising results

Once the models are fitted, summarise bias and dispersion across simulations:

    summary <- summarise_results(res)
    print(summary)

This function extracts model estimates and computes metrics such as mean absolute deviation (MADm) across replicates.

## Example output (simulated workflow)

Here’s a minimal example with few iterations for a fast runtime:

    sim <- simulate_data(ind = 50, partners = 2, iterations = 4, Valpha = 0.2, Vepsilon = 0.1)
    res <- run_model(sim, model = "Trait.stan", iter = 500, cores = 4)
    summary <- summarise_results(res)
    print(summary)

## Conclusion

The socialSim package helps researchers design, simulate, and evaluate models of social phenotypes and indirect genetic effects.

    ?simulate_data
    ?run_model
    ?summarise_results

and visit the GitHub page for the latest updates.